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http://pingstudy.ucsd.edu/

A large multi-site pediatric MRI and genetics data resource to facilitate studies of the genomic landscape of the developing human brain. It includes information about the developing mental and emotional functions of the children to understand the genetic basis of individual differences in brain structure and connectivity, cognition, and personality. Investigators on the project are studying 1400 children between the ages of 3 and 20 years so that links between genetic variation and developing patterns of brain connectivity can be examined. Investigators interested in the effects of a particular gene will be able to search the database for any brain areas or connections between areas that differ as a function of variation in a particular gene, and also to determine if the genes appear to affect the course of brain development at some point during childhood. A data exploration tool has been created for mapping and analyzing MRI data sets collected for PING and related developmental studies. Approved investigators will be able to view raw image sets and derived 3D brain maps of MRI and DTI data, conduct hypothesis testing, and graph brain area measures as they change across the time course of development. PING Cores * Coordinating Core: Functions include project management, screening of participants and maintaining the database * Neuroimaging Core: applying a standardized high-resolution structural MRI protocol involving 3-D T1-weighted scans, a T2-weighted volume, and a set of diffusion-weighted scans with multiple b values and diffusion directions, scans to estimate MRI relaxation rates, and gradient echo EPI scans for resting state fMRI. Importantly, adaptive motion compensation, using ����??PROMO����??, a novel real-time motion correction algorithm will be used. Specific PING protocols for each scanner manufacturer: ** PING MRI Protocol - GE ** PING MRI Protocol - Philips ** PING MRI Protocol - Siemens * Assessment Core: Cognitive assessments for the PING project are conducted using the NIH Toolbox for Cognition. * Genomics Core: functions as a central repository for receipt of saliva samples collected for each study participant. Once received, samples are catalogued, maintained, and DNA is extracted using state-of-the-field laboratory techniques. Ultimately, genome-wide genotyping is performed on the extracted DNA using the Illumina Human660W-Quad BeadChip. PING involves 10 sites throughout the country including UCSD, University of Hawaii, Scripps Genomics, UCLA, UC Davis, Kennedy Krieger Institute/Johns Hopkins, Sacker Institute/Cornell University, University of Massachusetts, Massachusetts General Hospital/Harvard, and Yale. Families who may want to participate in the study, or others who want to know more about it, may email questions to ping (at) ucsd.edu.

Proper citation: Pediatric Imaging Neurocognition and Genetics (RRID:SCR_008953) Copy   

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http://recombineering.ncifcrf.gov/

Recombineering (recombination-mediated genetic engineering) is a powerful method for fast and efficient construction of vectors for subsequent manipulation of the mouse genome or for use in cell culture experiments. It is also an efficient way of manipulating the bacterial genome directly. Recombineering is a method based on homologous recombination in E. Coli using recombination proteins provided from ? phage. Our bacterial strains contain a defective ? prophage inserted into the bacterial genome. The phage genes of interest, exo, bet, and gam, are transcribed from the ?PL promoter. This promoter is repressed by the temperature-sensitive repressor cI857 at 32C and derepressed (the repressor is inactive) at 42C. When bacteria containing this prophage are kept at 32C no recombination proteins are produced. However, after a brief (15 minutes) heat-shock at 42C a sufficient amount of recombination proteins are produced. exo is a 5''-3'' exonuclease that creates single-stranded overhangs on introduced linear DNA. bet protects these overhangs and assists in the subsequent recombination process. gam prevents degradation of linear DNA by inhibiting E. Coli RecBCD protein. Linear DNA (PCR product, oligo, etc.) with sufficient homology in the 5'' and 3'' ends to a target DNA molecule already present in the bacteria (plasmid, BAC, or the bacterial genome itself) can be introduced into heat-shocked and electrocompetent bacteria using electroporation. The introduced DNA will now be modified by exo and bet and undergo homologous recombination with the target molecule. The method is so efficient that co-electroporation of a supercoiled plasmid and a linear piece of DNA into heat-shocked, electrocompetent bacteria will work as well.

Proper citation: Recombineering Information (RRID:SCR_008556) Copy   

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http://biobehavioural.blogspot.com/

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 6, 2016. Biobehavioral blog on research and medicine as a continuum from biological mechanisms to behavioural phenomena., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Biobehavioral (RRID:SCR_008710) Copy   

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http://mips.helmholtz-muenchen.de/funcatDB/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 19, 2019. The Functional Catalogue is an annotation scheme for the functional description of proteins of prokaryotic and eukaryotic origin. Taking into account the broad and highly diverse spectrum of known protein functions, the FunCat consists of 28 main functional categories (or branches) that cover general fields like cellular transport, metabolism and cellular communication/signal transduction. The main branches exhibit a hierarchical, tree like structure with up to six levels of increasing specificity. In total, the FunCat version 2.1 includes 1362 functional categories. This general concept was retained since the annotation of the Saccharomyces cerevisiae genome with only 4 revisions and later on also proved to be well suited for the annotation of genomes from different domains of life (Ruepp et al. 2004). The present and previous versions as well as a version mapping file of the FunCat and annotation data of our core projects can be downloaded via FTP. The MIPS Functional Catalogue Database provides a search tool to browse and search the Functional Categories including the FunCat Number, description, EC number, GO number or keywords associated with the categories. All FunCat annotated proteins and the amount of Co-annotated-FunCats can be retrieved starting with a specific category in a selected organism. A statistical survey of the functional distribution of a given set of genes/entries, e. g. a set of genes with up-regulated expression under a certain condition can be retrieved.

Proper citation: MIPS FunCat (RRID:SCR_008709) Copy   

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http://www.icpsr.umich.edu/icpsrweb/NACDA/Pledge/all.jsp

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

Proper citation: Census Microdata Samples Project (RRID:SCR_008902) Copy   

•   Source: SciCrunch Registry

http://bionot.askhermes.org/integrated/BioNot.uwm

Database of negated biomedical sentences in literature consisting of more than 32 million negated sentences. Negated sentences were detected using the algorithm described in - Shashank Agarwal, Hong Yu Biomedical negation scope detection with Conditional Random Fields Journal of the American Medical Informatics Association (JAMIA), 2010; 17:696-701. After entering your query in the search box (for example MeCP2 autism), the search results with the negated sentence and the sentences preceding and following the negated sentences are displayed. A link to the source of the sentence is also provided, which links to the article from which the negated sentence was extracted. BioNOT is no longer updated. Documented 2013.

Proper citation: BioNOT (RRID:SCR_008743) Copy   

•   Source: SciCrunch Registry

http://empai.iab.keio.ac.jp/

emPAI (exponentially modified protein abundance index), developed by Ishihama et al., is a measure to describe the protein composition in sample solutions. When the total protein amount in the sample is available, emPAI can be converted to the absolute amount of each protein in the sample. emPAI is derived from PAI, which is defined as the number of the observed peptides divided by the number of the observable peptides per protein. We recently found that log (PAI) had linear relationship to the protein amounts, and that emPAI, 10^(PAI)-1, was proportional to the protein amounts for whole cell lysate digested by trypsin. The accuracy of this method was within factor 5, similar or better than determination of abundance by protein staining

Proper citation: Exponentially Modified Protein Abundance Index (RRID:SCR_008616) Copy   

•   Source: SciCrunch Registry

http://lsid.tdwg.org/

A web based life sciences identifier (LSID) resolution service allows you to view the data and metadata of an LSID with a web browser. This service will display the metadata, formatted as a standard webpage, for any LSID.

Proper citation: LSID Web Resolver (RRID:SCR_009737) Copy   

•   Source: SciCrunch Registry

http://neuromorphometrics.com/?page_id=23

Collection of neuroanatomically labeled MRI brain scans, created by neuroanatomical experts. Regions of interest include the sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter and sub-divided cortex into parcellation units that are defined by gyral and sulcal landmarks.

Proper citation: Manually Labeled MRI Brain Scan Database (RRID:SCR_009604) Copy   

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http://wren.bcf.ku.edu/

The Autism Genetic Database currently contains the full list of autism susceptibility genes as well as all Copy Number Variations (CNVs) found to have a relationship to autism. Additionally, all noncoding RNA molecules (snoRNA, miRNA, and piRNA) and chemically induced fragile sites are stored as well. This information is currently accessible via an in-house human genome browser focusing specifically on the chromosomal features associated with autism, and in a tabular format broken down by chromosome. Genome Browser:A genome browser that displays the genes, CNVs, ncRNAs and fragile sites in an easily accessible graphical visualization tool Tabular Data Display:A tabular data display that allows the user to observe the chromosomal spatial relationship between the genes, CNVs, ncRNAs and fragile sites. This also provides links to Entrez and pubmed for each gene, as well as miRBase for miRNAs, snoRNA-LBME-db for snoRNAs, and piRNABank for piRNAs.

Proper citation: Autism Genetic Database (RRID:SCR_010545) Copy   

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http://virginiahughes.com/

Virginia Hughes'' Research Blog

Proper citation: http://virginiahughes.com/ (RRID:SCR_010558) Copy   

•   Source: SciCrunch Registry

http://bis.zju.edu.cn/DaTo/

A biological database and software tool catalog based on text mined and human annotated url mentions in PubMed abstracts. Data are annotated as to the author''''s country of origin and url status is checked.

Proper citation: DaTo (RRID:SCR_010280) Copy   

•   Source: SciCrunch Registry

http://www.citeab.com/

Citation-ranked antibody search engine that provides a simple way to find antibodies that work. They use the number of citations as a transparent method to rank antibodies. Nobody can pay to be ranked higher. They are always looking for more commercial and academic antibodies to make CiteAb better. There is no charge to list.

Proper citation: CiteAb (RRID:SCR_009653) Copy   

•   Source: SciCrunch Registry

https://www.tycho.pitt.edu/

Database to advance the availability and use of public health data for science and policy making that includes data from all weekly notifiable disease reports for the United States dating back to 1888. Additional U.S. and international data will be released twice yearly.

Proper citation: Project Tycho (RRID:SCR_010489) Copy   

•   Source: SciCrunch Registry

http://www.accessdata.fda.gov/scripts/opdlisting/oopd/index.cfm

Database of Orphan Drug Product designations. Searches may be run by entering the product name, orphan designation, and dates. Results can be displayed as a condensed list, detailed list, or an Excel spreadsheet.

Proper citation: Search Orphan Drug Designations and Approvals (RRID:SCR_010256) Copy   

•   Source: SciCrunch Registry

http://www.hgsc.bcm.tmc.edu/content/hapmap-3-and-encode-3

Draft release 3 for genome-wide SNP genotyping and targeted sequencing in DNA samples from a variety of human populations (sometimes referred to as the HapMap 3 samples). This release contains the following data: * SNP genotype data generated from 1184 samples, collected using two platforms: the Illumina Human1M (by the Wellcome Trust Sanger Institute) and the Affymetrix SNP 6.0 (by the Broad Institute). Data from the two platforms have been merged for this release. * PCR-based resequencing data (by Baylor College of Medicine Human Genome Sequencing Center) across ten 100-kb regions (collectively referred to as ENCODE 3) in 712 samples. Since this is a draft release, please check this site regularly for updates and new releases. The HapMap 3 sample collection comprises 1,301 samples (including the original 270 samples used in Phase I and II of the International HapMap Project) from 11 populations, listed below alphabetically by their 3-letter labels. Five of the ten ENCODE 3 regions overlap with the HapMap-ENCODE regions; the other five are regions selected at random from the ENCODE target regions (excluding the 10 HapMap-ENCODE regions). All ENCODE 3 regions are 100-kb in size, and are centered within each respective ENCODE region. The HapMap 3 and ENCORE 3 data are downloadable from the ftp site.

Proper citation: HapMap 3 and ENCODE 3 (RRID:SCR_004563) Copy   

•   Source: SciCrunch Registry

http://www.cas.org/expertise/cascontent/registry/index.html

CAS REGISTRY, the gold standard for substance information, is the only integrated, comprehensive source of chemical information from a full range of disclosed material including patents, journals, and reputable web sources. When you need to positively identify a chemical substance, you can rely on the authoritative source for chemical names and structures of CAS REGISTRY. CAS databases are curated and quality-controlled by CAS scientists, and recognized by chemical and pharmaceutical companies, universities, government organizations, and patent offices around the world as authoritative. By combining these databases with advanced search and analysis technologies (SciFinder, STN, and Science IP products and services), CAS delivers the most current, complete, and cross-linked secure digital information environment for scientific discovery. You can identify your substance of interest by its CAS Registry Number, which is the best way to identify a substance, regardless of what name you have for it. You can also use CAS REGISTRY to locate * literature references to the substance * experimental and predicted property data (boiling and melting points, etc.) * commercial availability * preparative methods * spectra * regulatory information from international sources

Proper citation: CAS REGISTRY (RRID:SCR_004558) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/dbfetch/dbfetch

Dbfetch is an acronym for database fetch. Dbfetch provides an easy way to retrieve entries from various databases at the EBI in a consistent manner and allows you to retrieve up to 50 entries at a time from various up-to-date biological databases. It can be used from any browser as well as well as within a web-aware scripting tool that uses wget, lynx or similar. From the browser, follow these instructions... * Select a database: If you are using the first form to paste your search items: choose a database name from this form. If you are using the second form to upload your search items: the database name is included at the beginning of each line line of the upload file followed by a colon. * Enter search terms: These MUST BE in the appropriate database format, up to 200 search items can be queried in one run. If you are using the first form: separate search items with a comma or space. If you are using the second form: separate search items with a new line. * Choose an output format: Here you can choose the simpler fasta format, or the databases'''' default format for the chosen database. * Style: You can get your results as text or html. * Retrieve! - You are now ready to fetch your results, by pressing the Retrieve button.

Proper citation: EBI Dbfetch (RRID:SCR_004393) Copy   

•   Source: SciCrunch Registry

http://integromedb.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016. Search engine that integrates over 100 curated and publicly contributed data sources and provides integrated views on the genomic, proteomic, transcriptomic, genetic and functional information currently available. Information featured in the database includes gene function, orthologies, gene expression, pathways and protein-protein interactions, mutations and SNPs, disease relationships, related drugs and compounds.

Proper citation: IntegromeDB (RRID:SCR_004620) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/sites/entrez?db=geo

The GEO Profiles database stores gene expression profiles derived from curated GEO DataSets. Each Profile is presented as a chart that displays the expression level of one gene across all Samples within a DataSet. Experimental context is provided in the bars along the bottom of the charts making it possible to see at a glance whether a gene is differentially expressed across different experimental conditions. Profiles have various types of links including internal links that connect genes that exhibit similar behaviour, and external links to relevant records in other NCBI databases. GEO Profiles can be searched using many different attributes including keywords, gene symbols, gene names, GenBank accession numbers, or Profiles flagged as being differentially expressed.

Proper citation: Entrez GEO Profiles (RRID:SCR_004584) Copy   

•   Source: SciCrunch Registry